Abstract: The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

Abstract: A method for urban scene reconstruction uses the top view of a scene as priori information to generate a UVA initial flight path, optimizes the initial path in real time, and realizes 3D reconstruction of the urban scene. There are four steps: (1): to analyze the top view of a scene, obtain the scene layout, and generate a UAV initial path; (2): to reconstruct the sparse point cloud of the building and estimate the building height according to the initial path, combine the scene layout to generate a rough scene model, and adjust the initial path height; (3): to use the rough scene model, sparse point cloud and the UAV flight trajectory to obtain the scene coverage confidence map and the details that need close-ups, optimize the flight path in real time; and (4): to obtain high resolution images, reconstruct them to obtain a 3D model of the scene.

Abstract: A method for urban scene reconstruction uses the top view of a scene as priori information to generate a UVA initial flight path, optimizes the initial path in real time, and realizes 3D reconstruction of the urban scene. There are four steps: (1): to analyze the top view of a scene, obtain the scene layout, and generate a UAV initial path; (2): to reconstruct the sparse point cloud of the building and estimate the building height according to the initial path, combine the scene layout to generate a rough scene model, and adjust the initial path height; (3): to use the rough scene model, sparse point cloud and the UAV flight trajectory to obtain the scene coverage confidence map and the details that need close-ups, optimize the flight path in real time; and (4): to obtain high resolution images, reconstruct them to obtain a 3D model of the scene.

Abstract: The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.